AI ML Engineering Jobs at Google with Visa Sponsorship
Google hires AI ML Engineers across research, product, and infrastructure teams, working on large-scale models, ML infrastructure, and applied AI systems. The company has a consistent track record of sponsoring work visas for this function, making it a realistic target if you're on H-1B, H-1B1, or E-3 status.
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Get Access To All JobsINTRODUCTION
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Raleigh, NC, USA; Durham, NC, USA.
MINIMUM QUALIFICATIONS:
- Bachelor’s degree, or equivalent practical experience.
- 8 years of experience in software development.
- 3 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
- 3 years of experience in a technical leadership role.
- 2 years of experience in a people management or team leadership role.
- Experience developing software applications using the C++ programming language.
PREFERRED QUALIFICATIONS:
- Master's degree or PhD in Computer Science or a related technical field.
- 3 years of experience working in a complex, matrixed organization.
- Experience with Nvidia Collective Communications Library (NCCL), Nvidia Index Library (NIXL), Deep Learning Execution Provider (DeepEP), and Mooncake.
ABOUT THE JOB
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started - and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
Network Infrastructure Team's mission is to be an expert in the hardware-software interface, and to influence the co-design of Google software and hardware to strike the right balance between performance-optimized hardware and the implications of the API design on software performance and maintainability.
In this role, you will work with hardware designers, vendors, and Google software teams alike, you will think about the software and hardware performance, and how API design affects these. You will work on specific offload technologies, including AI Training and Inference Transport Layers as well as dataplane encryption. The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Set and communicate team priorities that support the broader organization's goals.
- Manage and lead the team that is developing the next generation Artificial Intelligence (AI) and Machine Learning (ML) Networking solutions as well as Smart Network Interface Cards (NICs) at Google, taking the projects through development into production.
- Enable the team to advance new approaches to leverage offloads efficiently with Google hardware and software.
- Guide the team to build and test software in C++ for use on Google's Machine Learning (ML) Library solutions and Smart Network Interface Cards (NICs).
- Align strategy, processes, and decision-making across teams.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
INTRODUCTION
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Raleigh, NC, USA; Durham, NC, USA.
MINIMUM QUALIFICATIONS:
- Bachelor’s degree, or equivalent practical experience.
- 8 years of experience in software development.
- 3 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
- 3 years of experience in a technical leadership role.
- 2 years of experience in a people management or team leadership role.
- Experience developing software applications using the C++ programming language.
PREFERRED QUALIFICATIONS:
- Master's degree or PhD in Computer Science or a related technical field.
- 3 years of experience working in a complex, matrixed organization.
- Experience with Nvidia Collective Communications Library (NCCL), Nvidia Index Library (NIXL), Deep Learning Execution Provider (DeepEP), and Mooncake.
ABOUT THE JOB
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started - and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
Network Infrastructure Team's mission is to be an expert in the hardware-software interface, and to influence the co-design of Google software and hardware to strike the right balance between performance-optimized hardware and the implications of the API design on software performance and maintainability.
In this role, you will work with hardware designers, vendors, and Google software teams alike, you will think about the software and hardware performance, and how API design affects these. You will work on specific offload technologies, including AI Training and Inference Transport Layers as well as dataplane encryption. The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Set and communicate team priorities that support the broader organization's goals.
- Manage and lead the team that is developing the next generation Artificial Intelligence (AI) and Machine Learning (ML) Networking solutions as well as Smart Network Interface Cards (NICs) at Google, taking the projects through development into production.
- Enable the team to advance new approaches to leverage offloads efficiently with Google hardware and software.
- Guide the team to build and test software in C++ for use on Google's Machine Learning (ML) Library solutions and Smart Network Interface Cards (NICs).
- Align strategy, processes, and decision-making across teams.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
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Get Access To All JobsTips for Finding AI ML Engineering Jobs at Google Jobs
Align your portfolio to Google's AI research areas
Google prioritizes ML engineers with experience in large language models, distributed training infrastructure, or ML compilers. Tailor your GitHub, publications, or project work to these domains before applying, since hiring committees assess technical fit against active research priorities.
Target teams that file LCAs year-round
Google's AI and Research org files Labor Condition Applications with DOL continuously, not just during H-1B cap season. Roles under Google DeepMind, Google Research, and core ML infrastructure teams are more likely to have active sponsorship pipelines outside of April.
Use Migrate Mate to filter open roles by visa type
Google posts AI ML Engineering roles across multiple teams simultaneously, and not every opening is tied to a sponsoring team. Use Migrate Mate to filter specifically for roles where H-1B, H-1B1, or E-3 sponsorship is confirmed.
Prepare for Google's structured technical interview format
Google uses a standardized interview loop for ML roles: coding rounds, ML system design, and a research depth interview. Prepare ML system design answers that reference real production constraints, not just academic benchmarks, since interviewers expect applied scale thinking.
Clarify E-3 or H-1B1 eligibility early with your recruiter
E-3 and H-1B1 visas sit outside the annual H-1B lottery cap and can be processed faster. If you're an Australian or Singapore or Chile national, confirm with Google's immigration team that your target role qualifies as a specialty occupation under the correct visa category.
AI ML Engineering at Google jobs are hiring across the US. Find yours.
Find AI ML Engineering at Google JobsFrequently Asked Questions
Does Google sponsor H-1B visas for AI ML Engineers?
Yes, Google sponsors H-1B visas for AI ML Engineering roles. Google also sponsors H-1B1 visas for nationals of Singapore and Chile, and E-3 visas for Australian nationals. Sponsorship is handled through Google's internal immigration team in coordination with outside counsel, and the process typically begins after an offer is extended.
Which visa types does Google commonly use for AI ML Engineering roles?
Google sponsors H-1B, H-1B1, and E-3 visas for AI ML Engineering positions. H-1B is subject to the annual lottery cap, with registration in March and an October 1 start date. H-1B1 and E-3 are cap-exempt and can be filed at any point in the year, which makes them faster options for eligible nationals.
What qualifications does Google expect for AI ML Engineering roles?
Google generally expects a bachelor's degree in computer science, electrical engineering, or a related field, with a master's or PhD preferred for research-adjacent roles. Practical experience with ML frameworks like JAX, TensorFlow, or PyTorch is expected. Roles on Google DeepMind or Google Research teams often require demonstrated publication history or equivalent industry research output.
How do I apply for AI ML Engineering jobs at Google?
Applications go through Google's careers portal at careers.google.com. Roles are posted by team, so filtering by function and location matters. If you need visa sponsorship, Migrate Mate lets you browse open AI ML Engineering positions at Google filtered by visa type, so you can focus on roles where sponsorship is confirmed before you apply.
How do I understand the H-1B filing timeline if I get an offer from Google?
If you're subject to the H-1B cap, Google must register you in USCIS's annual lottery, which opens in March. Selection is random, and if chosen, the petition is filed by June for an October 1 start date. If you're already in H-1B status with another employer, Google can file a transfer petition outside cap season with no lottery required.
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